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    Agent-native Amazon review intelligence — fetches verified reviews from 10 marketplaces via real Shulex OpenAPI (not scrapers) and produces copy-ready listing improvements grounded in actual customer language. Backed by a 2B-review historical dataset that Helium 10 / Jungle Scout can't replicate. Works in any MCP client (Claude Code, Claude Desktop, ChatGPT, Cursor, Windsurf, VS Code, Cline).
    6
    33
    MIT
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    Enables AI assistants to search and filter Azure platform updates, including previews, general availability, and retirements, through natural language queries.
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    MIT
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    A cross-AI-agent local task context sharing MCP server that allows clients like Codex, Claude Code, Hermes, and VS Code to read and write the same task state, enabling task tracking and context distillation.
    6
    MIT
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    Enables LLMs to query Azure service pricing via the public Azure Retail Prices API, with tools for searching prices, estimating costs, comparing regions, and listing services.
    5
    MIT
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    An MCP server for Azure development and operations, enabling Cosmos DB queries, Service Bus messaging, and PIM role activation.
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    MIT
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    An MCP server for Azure DevOps that allows users to manage work items, sprints, iterations, and attachments. It supports custom WIQL queries and includes pre-defined prompts for common tasks like sprint planning, bug reporting, and daily standup generation.
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    MIT
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    MCP server for on-premises Azure DevOps that lets AI assistants browse repositories, review pull requests, manage work items, and interact with wikis, with NTLM authentication support.
    35
    Apache 2.0
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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    Enables AI agents to autonomously manage and improve execution processes for repetitive task types by storing reusable task contexts with associated artifacts (practices, rules, prompts, learnings) and providing full-text search across historical best practices.
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    MIT